Malik
Diyaolu
Focused on building intelligent, scalable solutions that bridge machine learning and real-world applications.
Experience
AI/ML Engineer
Spearheaded development of AI-driven solutions and optimized AI trading system architectures. Designed and built comprehensive bot platforms handling full lifecycle from concept to deployment.
Product ML Engineer
Led AI-driven lead generation from social media using scraping algorithms and data management. Optimized lead generation with Redis, Next.js, Python, and APIFY.
Machine Learning Software Engineer
Developed food GPT MVP with RAG interface using Llama and GPT-3.5 for optimal user experience.
Full-Stack Software Engineer
Redesigned website sections for improved UX and focused on AI integration for existing platforms.
Software Engineer / ML Intern
Improved frontend-backend API interaction and enhanced AI capabilities with NLTK. Achieved 18% performance increase in Vendor-Client matching service.
Software Engineer
Developed Admin dashboard for pharmacy performance analysis. Optimized product management by 60% and streamlined stock-taking by 35%.
Full-Stack Developer
Developed responsive web application using React.js and Node.js. Integrated Twilio for promotional and transactional SMS.
Backend Developer
Developed 100+ RESTful API endpoints. Implemented real-time vehicle tracking using AWS SQS, SNS, Redis, and Apache Kafka, reducing development time by 50%.
Co-Founder & Backend Engineer
Built RESTful APIs for mobile app supporting dynamic data across multiple countries. Improved business workflows through messaging and notification services.
Software Engineer Intern
Built foundational skills in Python, PHP, and JavaScript while developing customized WordPress website for interior design company.
Technical Projects
The Oxy7d
An integrated platform housing my portfolio, user authentication system, and support infrastructure. Supporters gain complimentary access to hosted ML projects, creating a sustainable model for open-source development and community engagement.
Lumen
Large language model research focused on low-resource African languages. Pre-trained architecture optimized for empathetic responses, addressing linguistic diversity gaps in current AI systems while preserving cultural nuances.
FLARE
Federated Learning framework enabling collaborative model training across organizational boundaries. Participants train locally and share encrypted gradient updates instead of raw data, maintaining privacy while advancing collective intelligence.
Recent Thoughts
The Future of Low-Resource LLMs
Dec 15, 2024Privacy in Distributed Learning
Nov 28, 2024Let's Connect
Open to collaborations in AI/ML, Systems Programming, and scalable backend architecture.